Context-aware privacy meter

ABSTRACT

Technologies are presented that provide context-aware privacy metering regarding establishments, such as consumer establishments. A method of rating privacy of one or more establishments may include receiving a location designation of a user device from the user device (automatically or via user input), obtaining privacy-related information regarding one or more establishments in proximity of the location designation, and generating one or more privacy score vector algorithms for the one or more establishments based on the privacy-related information regarding the one or more establishments. The method may further include obtaining a privacy profile of the user, determining one or more privacy scores for the one or more establishments by applying the privacy profile of the user to the one or more privacy score vector algorithms. The method may further include generating and providing a privacy-related recommendation regarding a particular establishment.

BACKGROUND

Many business establishments that consumers patronize, such as retailstores, restaurants, etc., try to monitor their customers and theirbehavior. Many offer loyalty-based membership cards and/or credit cardsoffering consumers discounts and special offers. However, these“memberships” are often used to track and profile customers, bymonitoring store visits, purchases made, etc. Consumers are usuallyrequired to register for these memberships using personal information,and the personal information in conjunction with the subsequentlycollected consumer behavioral data may allow the establishments to studytrends within certain demographics and markets, which may assist them inimproving their products and/or services. What else the businesses dowith this data is often a subject of debate, however. Many advocacygroups and consumers themselves may want to know what data is captured,how the data is used, whether it is sold or otherwise provided toothers, whether and when it is purged, etc. In addition to, or insteadof, loyalty memberships, establishments may monitor consumer behaviorusing other means, such as in-store security cameras, demographicprofiling, etc.

Many advocacy groups may attempt to keep track of privacy-relatedbehavior and practices of various establishments. However, these groupsmay tend to be very conservative and strict in their privacy ratings.The advocacy groups may require a consumer to proactively, andfrequently, visit their websites to stay updated on their studiedestablishments. Furthermore, there likely may not be a way for one ormore consumers to provide feedback in a way that influences the advocacygroups' opinions of establishments.

For average consumers, it is currently difficult to determine whatestablishments are capturing what information about them and theprivacy-related risks associated with each establishment. Moreover, theconcept of privacy has become very fluid and elastic where it may beperceived and accepted differently by different people. For some,privacy is of utmost concern, driving some to paranoia levels, whileothers are not concerned about it at all. It is expected, however, thatmost consumers are uncomfortable with their personal data being takenand used, especially if they may be unaware if or when it is even beingdone.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

FIG. 1 is a block diagram of an example system described herein,according to an embodiment.

FIGS. 2A-2C illustrate a sequence diagram of an example process flow asdescribed herein, according to an embodiment.

FIGS. 3A-3C illustrate a sequence diagram of an example process flow asdescribed herein, according to an embodiment.

FIG. 4 is a block diagram of an example consumer privacy rating server,according to an embodiment.

FIG. 5 is a block diagram of an example user device, according to anembodiment.

In the drawings, the leftmost digit(s) of a reference number mayidentify the drawing in which the reference number first appears.

DETAILED DESCRIPTION

When a consumer is about to “visit” an establishment, the consumer maywant to know ahead of time what privacy risks would be taken by doingso. By “visiting”, a consumer may, for example, be about to physicallywalk into a business establishment (such as, for example a retail storeor a restaurant), be planning on visiting an establishment at a latertime, or have just accessed a website, or web-based or downloadedapplication of, an establishment. The privacy risks that a consumer maybe interested in may pertain to, for example, what personal data may becollected by the establishment and/or how personal data is used by theestablishment, whether and/or where there are cameras in theestablishment and how they are used, etc. Currently, there are noservices that provide this information to a consumer in a direct andtimely manner.

Disclosed herein are technologies that solve the technical problem ofhow to provide a consumer with metrics, ratings, and/or recommendationsregarding privacy for the establishments that consumer would like tovisit, either physically or virtually, and either prior to arriving orjust as they are arriving at those establishments. The technologiesdisclosed herein determine privacy metrics that use publicly availableinformation and crowd-sourced data in conjunction with the user's owncontext and previous behavior. The privacy metrics may be used toprovide ratings and/or recommendations to the user with regard to whataction to take at the establishments the user would like to patronizebased on the user's own comfort level with regard to privacy.

Embodiments are now described with reference to the figures, where likereference numbers may indicate identical or functionally similarelements. While specific configurations and arrangements are discussed,it should be understood that this is done for illustrative purposesonly. A person skilled in the relevant art will recognize that otherconfigurations and arrangements can be used without departing from thespirit and scope of the description. It will be apparent to a personskilled in the relevant art that this can also be employed in a varietyof other systems and applications other than what is described herein.

FIG. 1 is a block diagram of an example system 100, according to anembodiment. System 100 may include a consumer privacy rating server 102,of a consumer privacy rating service, which may include, or have accessto, a data store 104 of privacy-related establishment information, andoptionally, a data store of user privacy profiles. System 100 may alsoinclude one or more data sources 110-1 to 110-M (collectively, 110),which may include various types of privacy-related information withregard to various establishments visited by consumers, such as businessestablishments and/or websites, retail stores and/or websites,restaurants, etc. System 100 may also include one or more user devices108-1 to 108-N (collectively, 108) of users of the consumer privacyrating service (e.g., consumer users who visit the variousestablishments that are rated by this service). The consumer privacyrating server 102, the data sources 110, and the user devices 108 may bein communication via a network 112.

The consumer privacy rating server 102 may include a privacy scoregenerator 103 that may be implemented in software and/or hardwareexecuted or controlled by a controller of the consumer privacy ratingserver 102. While only one consumer privacy rating server is illustratedfor clarity and ease of discussion, it should be appreciated that theconsumer privacy rating server may include multiple distributed servercomputers for redundancy and/or load sharing, for example.

The user devices 108 may be computing devices that may include mobileand non-mobile devices. Mobile devices may include, but are not to belimited to, for example, laptop computers, ultra-laptop computers,tablets, touch pads, portable computers, handheld computers, palmtopcomputers, personal digital assistants (PDAs), e-readers, cellulartelephones, combination cellular telephone/PDAs, mobile smart devices(e.g., smart phones, smart tablets, etc.), mobile internet devices(MIDs), mobile messaging devices, mobile data communication devices,mobile media playing devices, cameras, mobile gaming consoles, etc.Non-mobile devices may include, but are not to be limited to, forexample, personal computers (PCs), televisions, smart televisions, datacommunication devices, media playing devices, gaming consoles, etc. Theuser devices 108 are user devices (e.g., personal user devices ofconsumers) that may include controllers and other components thatexecute software and/or control hardware in order to execute localprograms or consume services provided by external service providers overa network. For example, the user devices 108 may include one or moresoftware clients or applications for utilizing or accessing web-basedservices (e.g., online stores or services, social networking services,etc.). The user devices 108 may also, or instead, include a webinterface running in a browser from which the user device can accesssuch web-based services. User devices 108 may also include storagedevices 109-1 to 109-N (collectively 109) to store logic and dataassociated with the programs and services used by the users of the userdevices.

The network 112 may be any wired or wireless network, such as a WideArea Network (WAN), a Local Area Network (LAN), and/or the like. As anexample, the network 112 may be a distributed public network, such asthe Internet, where the consumer privacy rating server 102 and the userdevices 108 are connected to the network 112 via wired or wirelessconnections.

System 100 may optionally also include a communication link from theestablishments 114 themselves to the consumer privacy rating server 102.As an example, an establishment may be particularly proud of its ratingby the consumer privacy rating service, and may wish to have directaccess to their rating on display at a kiosk in the store. At such akiosk, a user may also be able to access the user's account in order toview a privacy rating for the establishment customized to that user'spreferences, etc.

In embodiments described more fully below, the consumer privacy ratingservice may determine privacy score vector algorithms for a particularestablishment based on privacy-related information it has collected forthat establishment and stored in data store 104. The privacy-relatedestablishment information may be collected by the consumer privacyrating server 102 in various ways. For example, data agents may beregularly or occasionally spawned by consumer privacy rating server 102to crawl the World Wide Web, via network 112, for privacy-relatedestablishment information from data sources 110. Data sources 110 mayinclude, but are not to be limited to, websites and other data sourcesof, for example, advocacy groups, watchdogs, privacy-related blogs, newsservices, or establishments themselves. The data collected from the datasources may include, but are not to be limited to, for example,electronically accessible privacy statements of establishments, pledgesto consumers by establishments, data policies of the establishments,warnings regarding establishments, news stories regardingestablishments, etc.

Privacy-related establishment information may also be received by theconsumer privacy rating server 102 from users of the consumer privacyrating service. For example, users may be provided with the capabilityto submit various types of information regarding establishmentsincluding, but not to be limited to, for example, photographs, videos,positive feedback, negative feedback, general comments, etc. A user maywant to provide this information because the user witnessed somethingduring the user's experience with the establishment that it believesshould be reported to the consumer privacy rating service. In anembodiment, the consumer privacy rating server 102 may alsoautomatically collect information regarding actual behavior of userswith regard to the establishments that they have visited. For example,the consumer privacy rating server 102 may automatically detect that auser never visits a particular establishment with the user's children,which may indicate that the user may not be comfortable bringingchildren to that establishment for privacy-related reasons. In anotherexample, the consumer privacy rating server 102 may automatically detectthat a user regularly uses an establishment loyalty card or credit card,which may indicate that the user is fairly comfortable with thatestablishment with regard to privacy. In another example embodiment, theconsumer privacy rating server 102 may monitor audio, and may determinewhether a user shared personal information (e.g., a zip code, and emailaddress, a phone number, etc.) In yet another example embodiment, theconsumer privacy rating server 102 may monitor electronic communications(e.g., email) of the user. In such an embodiment, the consumer privacyrating server 102 may determine that the user does not receive anyemails from a particular establishment, which may indicate that the userdid not share that type of information with the establishment. Thesetypes of determinations are pertinent to a particular user, but may alsobe used by the consumer privacy rating server 102 to establishcollective data regarding establishments that would benefit other userswhen determining privacy score vector algorithms for thoseestablishments for those other users.

A consumer may become a user of the consumer privacy rating service byregistering for the service through, for example, client software thatis downloaded and run on a user device 108 or a web-based client runningin a browser on a user device 108. When a user registers, aside frompossibly having a user provide identification and contact information, auser may be asked to answer some privacy-related questions in order toestablish an initial user privacy profile and comfort level. Thequestions may relate to, for example, user preferences regardingprivacy, information regarding the user's experience with variousestablishments or other establishments like the various establishments,etc. The user-provided preferences may include, for example, the user'scomfort level with cameras or other surveillance devices used atestablishments; the user's comfort level with one or more of facialanalysis, gender recognition, gender profiling, age recognition, ageprofiling, or dress profiling; the user's comfort level with providingto establishments one or more of the user's name, the user's birthdate,the user's phone number, the user's email address, the user's zip code,the user's gender, the names of the user's children, or the ages of theuser's children; the user's comfort level with allowing establishmentsto keep track of the user's transactions and for how long; etc. Theuser-provided experience information may include, for example, the typesof establishments the user frequents, the types of transactions the userconducts at those establishments, information regarding the user'sprivacy-related experiences at various establishments, etc. Furtherquestions may also be asked depending on the user's answers to previousquestions. In an alternative embodiment, a user may choose not have auser privacy profile created.

In embodiments, the user privacy profile may be edited by the userand/or may be automatically updated based on automatically collectedinformation regarding the user's actual behavior in an establishment.For example, the consumer privacy rating service may create a userprivacy profile for a user based on the user's preference and/orexperience input at the time of user registration. The user may view theuser's profile and think the profile has characterized the user asslightly more concerned about privacy than the user really is. The usermay be able to adjust the settings of the user privacy profile to moreaccurately represent the user's positions regarding privacy. Thisprofile adjustment may be done in various ways, such as by adjustingsliders, numerical settings, high/medium/low settings, mapping settingsto those of another user, etc. These types of profile adjustments wouldbe understood by those skilled in the art and are not discussed furtherhere.

The automatic collection of information regarding the user's actualbehavior in an establishment may also be used to update the user'sprivacy profile. For example, if a user's privacy profile indicates thatthe user wants to avoid establishments that use cameras to profilepeople, but the consumer privacy rating service determines automaticallythat a user shopped in a retail store, for example, despite a warningnot to enter the store due to the usage of cameras, the consumer privacyrating service may adjust the user's profile setting to one that is moretolerant of camera usage and/or of that particular store.

Usage of the consumer privacy rating service system will now bediscussed. Examples used to discuss the system's usage may involvereferences to a user entering a retail store for ease of description andunderstanding. However, usage of the system is not to be limited to userinteraction with retail stores. Many other types of establishments andinteractions therewith, including visits to virtual establishments(websites of establishments, etc.), may also be contemplated within thescope of this disclosure.

FIGS. 2A-2C illustrate a sequence diagram of an example process flow ofthe consumer privacy rating service, according to embodiments. In theseembodiments, the consumer privacy rating server determines both aprivacy score vector algorithm and, subsequently, a privacy score orrating for one or more establishments. In other embodiments (e.g.,discussed below with regard to FIGS. 3A-3C), the consumer privacy ratingserver may determine privacy score vector algorithms, with the privacyscores for establishments determined at the user device.

In FIG. 2A, user device 208 may receive user privacy preferences and/orexperience information (220) from the user, via, for example, a userinterface of the user device. The user privacy preferences andexperience information may be provided by the user when the userregisters for the consumer privacy rating service, or alternatively, ifthis information is already established, the user may update the userprivacy preferences and experience information via a user interface ofuser device 208 running a consumer privacy rating service clientapplication. The user privacy preferences and experience information maybe provided to the consumer privacy rating server 202 (222). Theconsumer privacy rating server 202 may update privacy-relatedestablishment information (224) that may be collected and maintained bythe consumer privacy rating server 202, based on the received userprivacy preferences and/or experience information. In an embodiment, auser privacy profile of the user may be maintained by the consumerprivacy rating server 202, in which case the user privacy profile may beupdated by the consumer privacy rating server 202 based on the receiveduser privacy preferences and/or experience information (226A). In analternate embodiment, the user device 208 may maintain the user privacyprofile for the user, in which case user device 208 may update the userprivacy profile for the user based on the user privacy preferencesand/or experience information (226B). In yet another embodiment, boththe consumer privacy rating server 202 and the user device 208 maymaintain, or may occasionally synchronize, copies of the user privacyprofile of the user. In a still further embodiment, a user may choosenot to create a user privacy profile.

Consumer privacy rating server 202 may collect privacy-relatedestablishment information (228), as discussed above. In embodiments,this collection of privacy-related establishment information may occurat designated scheduled times, regularly (e.g., every night frommidnight to 3 am), occasionally (whenever there are no user requestspending), etc. These examples are not meant to be limiting.

Various user scenarios will now be discussed. As an example, a user maybe out shopping and may be about to enter a retail store. The user mayinitiate the consumer privacy rating service client application on theuser's mobile device (e.g., user device 208), or alternatively mayalready have the client application running on the user device. The userdevice 208 may send a location designation to the consumer privacyrating server 202 (230). The location designation may be, for example,global positioning system (GPS) coordinates of the location of the userdevice 208, or other location coordinates or designations of thelocation of the user device 208 (e.g., a “last known” location, locationinformation obtained by pinging nearby mobile devices or through a Wi-Fiaccess point, etc.). The consumer privacy rating server 202 maydetermine, based on the location designation, which establishment orestablishments the user may be near. For example, the consumer privacyrating server 202 may determine the closest establishment to the user,or may determine that the user is within a given distance from a numberof establishments. As will be discussed in more detail below, the usermay then be provided with ratings and/or recommendations regardingprivacy for any or all of those nearby establishments.

As another example, a user may be planning a shopping trip for later inthe day, in which case the user may enter, using the consumer privacyrating client application or on a webpage of the consumer privacy ratingservice that the user has logged into, for example, a locationdesignation to be provided to the consumer privacy rating server 202,such as, for example, one or more zip codes of the area in which theuser plans to shop. In embodiments, other types of location designationsmay also be entered, such as for example, allowing the user to circle anarea on a map, naming a city, or naming one or more establishments of aspecific location. The consumer privacy rating server 202 may determine,based on the location designation, which establishment or establishmentsare within the area designated by the user. The user may then beprovided with ratings and/or recommendations regarding privacy for anyor all of those establishments. In an embodiment, the ratings and/orrecommendations may be for individual establishments (e.g., individualstores). In another embodiment, ratings may be provided on a heat map,where individual establishment ratings may be shown and/or a collectiverating for a particular area, which may be useful in rating a shoppingcenter or mall, for example.

In yet another example, the location designation provided to theconsumer privacy rating server 202 may be a virtual locationdesignation. In this example, the user may visit a website of anestablishment, such as a retail store or chain. If the consumer privacyrating service client application is running on the user device, it mayautomatically detect that the user is visiting a website of anestablishment for which privacy ratings and/or recommendations may beprovided. The user may then be provided with ratings and/orrecommendations regarding privacy for that particular establishment.

In the above descriptions are examples of being near or “in proximity”of one or more establishments. Being “in proximity” may have variousdefinitions. For example, to be “in proximity” of an establishment, thelocation represented by a location designation may be, but is not to belimited to, within a given distance of known coordinates of theestablishment, inside the establishment, at a website of theestablishment, within a user-entered zip code that includes theestablishment, etc.

Prior to determining privacy score(s) and/or recommendation(s), theconsumer privacy rating server 202 may obtain the user's privacy profilein order to customize the privacy score(s) and/or recommendation(s) forthe user. In an embodiment, if the consumer privacy rating server 202maintains user privacy profiles, then the consumer privacy rating server202 may obtain the user privacy profile from its local storage (232A).In another embodiment, if the user device 208 maintains the user privacyprofile, then the consumer privacy rating server 202 may request theuser privacy profile from user device 208 (232B), and receive the userprivacy profile from user device 208 (233). In an embodiment, genericprivacy rating(s) and/or recommendation(s) regarding establishments,without taking a user's privacy profile into consideration, may beprovided by the system (e.g., if the user chooses not to establish auser privacy profile); however, generic privacy rating(s) and/orrecommendation(s) may not be as useful to a user as customized privacyrating(s) and/or recommendation(s).

The consumer privacy rating server 202 may obtain privacy-relatedestablishment information from its local storage based on the receivedlocation designation (234). The privacy-related establishmentinformation may relate to particular establishments that fit thereceived designation information (e.g., establishments within a certaindistance of the location designation, establishments within one or morezip codes provided by the user, the establishment associated with thewebsite that the user is visiting, etc.).

The consumer privacy rating server 202 may generate one or more privacyscore vector algorithms based on the obtained privacy-relatedestablishment information (236). A privacy score vector algorithm mayprovide an algorithm from which a privacy score and one or morerecommendations may be generated with regard to a particularestablishment. As there are countless algorithms that may be used forthis purpose, as would be understood by those of skill in the art,specific vector algorithms will not be discussed here. A privacy scorevector algorithm may incorporate many various types of informationregarding an establishment, such as, but not to be limited to, forexample, extent of camera usage, extent of profiling using demographics(e.g., gender, age, etc.), extent of profiling using a loyalty programor store card (e.g., store credit card), extent of selling or sharing ofanonymized or non-anonymized data with other business entities (e.g.,business partners), extent of selling or sharing of data to otherparties, etc.

The consumer privacy rating server 202 may determine one or more privacyscore(s) regarding the relevant establishment(s) using the one or moreprivacy score vector algorithms (238). If a user privacy profile existsfor the user, the consumer privacy rating server 202 may apply the userprivacy profile to the privacy score vector algorithm(s) to determineone or more customized privacy scores for the user regarding therelevant establishments. In an embodiment, where customization using auser privacy profile is not done (e.g., if a user profile for a userdoes not exist, if the user opts to not set up a user profile, etc.), ageneric or default privacy profile may be applied to the privacy scorevector algorithm(s) to determine privacy score(s). A default privacyprofile may be, for example, a profile created using profiles of otherusers who may be considered similar to the user. In a still furtherembodiment, the privacy score vector algorithm(s) may be used todetermine privacy score(s) without applying any profile.

In an embodiment, a user privacy profile may be set by proxy where anentity sets a policy that will be adopted on one or more devices. In anexample, a company set may set privacy requirements and loggingcapabilities on mobile devices that it offers its employees for businessuse. In another example, a parent may set up a user privacy profile on amobile device for a minor (e.g., a teenager).

The determined privacy score(s) may be provided to the user device 208by the consumer privacy rating server 202 (240). User device 208 maydisplay the privacy score(s) to the user via a user interface on theuser device 208 (242) (e.g., via a pop-up display, a text message, anemail, a window of the consumer privacy rating service applicationrunning on user device 208, a webpage of the consumer privacy ratingservice, etc.). A privacy score may be, but is not to be limited to, forexample, a numerical rating (e.g., 1-5), a grade (e.g., A, B, C, D, F,or 0%-100%), an indication of high/medium/low, an object rating (e.g.,coloration of five stars), etc.

The discussion of the consumer privacy rating service continues withreference to FIG. 2B. Upon viewing the one or more privacy score(s), auser may want to know what information was behind a particular privacyscore, and may request such information from the consumer privacy server202 (244). For example, a user may click on the displayed rating or arequest button, for example, on the user interface to “drill down” tosee what information about a particular establishment the scorerepresented. The consumer privacy rating server 202 may receive therequest for details behind a privacy score and provide the requestedprivacy score details to user device 208 (246). User device 208 mayreceive and display the privacy score details (248).

In addition to, or instead of, generating one or more privacy scores,the consumer privacy rating server 202 may generate privacy-relatedrecommendations for the user with regard to the relevant establishments(250) based on the privacy-related establishment information for therelevant establishment, and if a user privacy profile exists, the userprivacy profile of the user. In embodiments, the generatedrecommendation(s) may be based on the privacy score vector algorithm,either alone or in conjunction with the user's privacy profile. Inembodiments, known recommendation algorithms, such as collaborativefiltering, may be used across the user population, for example, togenerate the recommendation(s). The generated recommendation(s) may beprovided to user device 208 (252). In embodiments, the recommendation(s)may be provided along with, or instead of, the determined privacyscore(s). In other embodiments, the recommendation(s) may be provided atthe request of the user, prior to, in conjunction with, after, orinstead of, the user device 208 being provided the privacy score(s).User device 208 may display the recommendation(s) (254). Arecommendation may include warning(s) or other types of informationregarding a particular establishment to help a user decide whetherand/or how to visit that establishment within their own comfort level.For example, a recommendation may recommend that the user enter theestablishment with caution because cameras are used in certain areas ofthe establishment and that data regarding the user will be tracked ifthe establishment loyalty card is used. In another example, if therecommendation is customized based on the user's privacy profile, andthe user's thresholds for cameras and data usage are very low, therecommendation for that user may be to not enter the store at all.

Some time after the user is provided with the privacy rating(s) and/orrecommendation(s) (e.g., within a given time period, such as one or morehours or days), the user may act in response to the privacy rating(s).For example, a user may follow a provided recommendation to not enter anestablishment. As another example, a user may enter an establishment anduse a loyalty card after being warned about the establishment's use ofcameras and collected data regarding the user. Based on, for example,location data (e.g., global positing system (GPS) location data),loyalty card usage data, etc., that may be provided by user device 208(either automatically or through direct user input, for example) toconsumer privacy rating server 202 (256), consumer privacy rating server202 may update the privacy-related establishment information for therelevant establishments (258). In an embodiment, if the consumer privacyrating server 202 maintains user privacy profiles, the consumer privacyrating server 202 may update the user privacy profile (260A). In anembodiment, if user device 208 maintains the user privacy profile forthe user, user device 208 may update the user privacy profile (260B).The user privacy profile for the user may be updated, or adjusted, basedon the user's actual behavior with regard to the establishment, if thethresholds set in the user privacy profile are too high or too low. Forexample, if the system recommended that the user not enter anestablishment due to camera use, but the user enters the establishmentdespite the recommendation, the user's threshold for camera usage in theuser privacy profile may automatically be adjusted. A threshold may alsochange after several instances of a user not following a recommendationin order to account for outliers. In addition, a rating of the store mayalso be influenced by this in order to reflect that consumers seem to becomfortable with a particular establishment despite privacy concerns. Inan embodiment, the consumer privacy rating service may show multipleratings (e.g., one based on mined and reported data, and another basedon shoppers' behavior).

The discussion of the consumer privacy rating service continues withreference to FIG. 2C. As stated earlier, in embodiments, the userprivacy profile may be edited by the user via the user interface of userdevice 208. In an embodiment, if user privacy profiles are maintained bythe consumer privacy rating server 202, the user device 208 may allowthe user to edit the user's user privacy profile and may provide theupdates to consumer privacy rating server 202 (262A). In an embodiment,if a user privacy profile is maintained by the user device 208, the userdevice 208 may allow the user to edit the user privacy profile to besaved locally at user device 208 (262B).

On occasion, the user may wish to provide the consumer privacy ratingservice with feedback regarding the user's experience with a particularestablishment. In an embodiment, user device 208 may allow the user toenter, through the user interface of user device 208, feedback regardingthe user's experience at a particular establishment (264), and userdevice 208 may provide the feedback to consumer privacy rating server202 (266). Consumer privacy rating server 202 may update theprivacy-related establishment information for that particularestablishment based on the provided feedback (268). In an embodiment, ifuser privacy profiles are maintained by the consumer privacy ratingserver 202, the consumer privacy rating server 202 may update the userprivacy profile of the user based on the provided feedback (270A). In anembodiment, if a user privacy profile is maintained by the user device208, the user device 208 may update the user privacy profile of the userbased on the provided feedback (270B). In an embodiment where both theconsumer privacy rating server 202 and the user device 208 maintain theuser privacy profile, both may update the user privacy profile of theuser based on the provided feedback. The feedback provided by the usermay include various types of information regarding a particularestablishment including, but not to be limited to, for example,photographs, videos, positive feedback, negative feedback, generalcomments, etc. The consumer privacy rating server 202 may be able tocategorize that feedback for subsequent use in generating privacy vectoralgorithms and/or privacy-related recommendations.

FIGS. 3A-3C illustrate a sequence diagram of an example process flow ofthe consumer privacy rating service, according to embodiments. Theseembodiments are similar to the embodiments shown with regard to FIGS.2A-2C, except that the consumer privacy rating server provides thegenerated privacy score vector algorithm(s) to the user device 208 todetermine the establishment privacy rating(s) and/or recommendation(s).

In FIG. 3A, similar to embodiments described with reference to FIG. 2A,a location designation may be provided by user device 308 to theconsumer privacy rating server 302, and consumer privacy rating server302 may obtain the relevant privacy-related establishment informationfrom local storage and generate one or more privacy score vectoralgorithm(s) for the relevant establishments. Here, however, thegenerated privacy score vector algorithm(s) are provided by the consumerprivacy rating server 302 to user device 308 (372). User device 308 mayobtain the user privacy profile for the user, if one exists, in order toapply the user privacy profile to the received privacy score vectoralgorithm(s). In an embodiment where user device 308 maintains the userprivacy profile, the user device 308 may obtain the user privacy profilefrom local storage (374A). In an embodiment where user privacy profilesare maintained by the consumer privacy rating server 302, user device308 may request the user privacy profile from consumer privacy ratingserver 302 (374B) and receive the user privacy profile from consumerprivacy rating server 302 (376). User device 308 may determine a privacyscore for each relevant establishment (378) by applying the user privacyprofile to the received privacy score vector algorithm(s). User device308 may then display the determined privacy score(s) (380).

The discussion of the consumer privacy rating service continues withreference to FIG. 3B. In an embodiment, user device 308 may receive arequest for details regarding a particular privacy score (382) from theuser. For example, upon viewing a displayed privacy score of aparticular establishment, the user may wish to “drill down” to see whatinformation that privacy score represented. The user may, for example,using the user interface of user device 308, click on the rating or adesignated button to request further details regarding a particularprivacy score. User device 308 may fetch and display the requested scoredetails (384).

In embodiments, user device 308 may generate one or more privacy-relatedrecommendations for the user with regard to the relevant establishments(386) based on the received privacy score vector algorithm, either aloneor in conjunction with the user's privacy profile, and display therecommendation(s) (388). In embodiments, the recommendation(s) may bedisplayed along with, or instead of, the determined privacy score(s). Inother embodiments, the recommendation(s) may be provided at the requestof the user. The recommendation(s) may be provide, prior to, inconjunction with, after, or instead of the privacy score(s). Asdescribed earlier, a recommendation may include warning(s) or othertypes of information regarding a particular establishment to help a userdecide whether and/or how to visit that establishment within their owncomfort level.

Other features illustrated in FIGS. 3A-3C are similar to the featuresdescribed above with reference to FIGS. 2A-2C and will not be describedagain here.

There are many additional useful features that may be incorporated intoa consumer privacy rating service such as the service described herein.For example, in an embodiment, a user may be able to specify that theuser wishes his or her user privacy profile to mirror that of a “friend”who is also a user of the service. The “friend” may be able to beidentified as having an established relationship with the user invarious ways. The “friend” may be able to be identified via anestablished relationship within a social networking service, forexample. In another example, the use may be able to enter identifyinginformation of the “friend” for lookup by the service (e.g., the“friend'”s user identification (ID) for the service, name, emailaddress, etc., and may allow the “friend” to verify the relationship. Instill another example, the relationship between the user and a “friend”could be established through an automatic close-proximity exchange ofinformation by user devices of the user and the “friend”, during whichthe devices may automatically exchange contact information, establishsocial networking relationships, etc. Many other ways of identifyingand/or verifying a relationship between two users may be contemplated,as would be understood by those skilled in the art.

In an embodiment, a user may be allowed to create multiple user privacyprofiles for differing circumstances or situations. For example, a usermay wish to maintain certain thresholds when visiting an establishmentalone or with another adult, but wish to maintain different thresholdswhen visiting an establishment with the user's children. In a relatedexample, the user may wish to maintain certain thresholds when visitinga certain type of establishment, but different thresholds when visitinganother type of establishment. In yet another example, a user may wishto create one or more specific user privacy profiles for specificestablishments. Many other circumstances or situations may also becontemplated where a user may wish to have differing user privacyprofiles.

In an embodiment, the determined privacy score(s) and/or privacy-relatedrecommendations may be customized and/or include information regardingthe establishment in general (e.g., an overall score or recommendationon a particular retail chain) or at a particular location (e.g., aspecific department store at a specific location). This may be importantand useful if the privacy practices of an establishment at a particularlocation fall short, or are better than, a generalized privacy overviewof the chain in which it is a part. A generalized overview of a chain,however, may be useful in situations where a user is shopping online, ormay be visiting an establishment that is very similar to anestablishment, or type of establishment, known by the service, but isnot known by the service itself.

In an embodiment, the determined privacy score(s) and/or privacy-relatedrecommendations provided to a user may not only be based on the user'spreferences, experiences, and/or behavior, but also those of users thatare “like” them. In this way, the service may be adaptive and generatesprivacy scores and/or recommendations based on crowd sourcing ofbehavior and consumer values rather than on hard set policies and rulesthat may be difficult to decipher, maintain, and change.

FIG. 4 is a block diagram of an example consumer privacy rating server402, according to an embodiment. The consumer privacy rating server 402may represent, for example, the consumer privacy rating servers 102,202, or 302 of FIGS. 1, 2A-2C, and 3A-3C, respectively. As illustrated,consumer privacy rating server 402 may include a processor or controller490 connected to memory 491, one or more secondary storage devices 492,and a communication interface 493 by a link 494 or similar mechanism.The consumer privacy rating server 402 may optionally include userinterface components 495 for use by a system or service administrator,for example, that may include, for example, a touchscreen, a display,one or more user input components (e.g., a keyboard, a mouse, etc.), aspeaker, or the like, or any combination thereof. Note, however, thatwhile not shown, consumer privacy rating server 402 may includeadditional components. In an embodiment, consumer privacy rating server402 may include a privacy score generator 403 to provide one or more ofthe functions described herein. Privacy score generator 403 may beseparate from processor 490 (as shown in FIG. 4), or may be a part ofprocessor 490. Processor 490 and/or privacy score generator 403 may be amicroprocessor, digital ASIC, FPGA, or similar hardware device. In anembodiment, the processor 490 and/or privacy score generator 403 may bea microprocessor, and software may be stored or loaded into the memory491 for execution by the processor 490 and/or privacy score generator403 to provide the functions described herein. The one or more secondarystorage devices 492 may be, for example, one or more hard drives or thelike, and may store logic 496 to be executed by the processor 490 and/orprivacy score generator 403. The communication interface 493 may beimplemented in hardware or a combination of hardware and software. Thecommunication interface 493 may provide a wired or wireless networkinterface to a network, such as the network 112 shown in FIG. 1.

FIG. 5 is a block diagram of an example user device 508, according to anembodiment. The user device 508 may represent, for example, the userdevices 108, 208, or 308 of FIGS. 1, 2A-2C, and 3A-3C, respectively. Asillustrated, user device 508 may include a processor or controller 590connected to memory 591, one or more secondary storage devices 592, anda communication interface 593 by a link 594 or similar mechanism. Theuser device 508 may also include user interface components 595 for useby a user of the user device (e.g., a consumer), that may include, forexample, a touchscreen, a display, one or more user input components(e.g., a keyboard, a mouse, etc.), a speaker, or the like, or anycombination thereof. Note, however, that while not shown, user device508 may include additional components. The processor 590 may be amicroprocessor, digital ASIC, FPGA, or similar hardware device. In anembodiment, the processor 590 may be a microprocessor, and software maybe stored or loaded into the memory 591 for execution by the processor590 to provide the functions described herein. The one or more secondarystorage devices 592 may be, for example, one or more hard drives or thelike, and may store logic 596 to be executed by the processor 590. Thecommunication interface 593 may be implemented in hardware or acombination of hardware and software. The communication interface 593may provide a wired or wireless network interface to a network, such asthe network 112 shown in FIG. 1.

Methods and systems are disclosed herein with the aid of functionalbuilding blocks illustrating functions, features, and relationshipsthereof. At least some of the boundaries of these functional buildingblocks have been arbitrarily defined herein for the convenience of thedescription. Alternate boundaries may be defined so long as thespecified functions and relationships thereof are appropriatelyperformed. While various embodiments are disclosed herein, it should beunderstood that they are presented as examples. The scope of the claimsshould not be limited by any of the example embodiments disclosedherein.

As discussed above, one or more features disclosed herein may beimplemented in hardware, software, firmware, and combinations thereof,including discrete and integrated circuit logic, application specificintegrated circuit (ASIC) logic, and microcontrollers, and may beimplemented as part of a domain-specific integrated circuit package, ora combination of integrated circuit packages. The terms software andfirmware, as used herein, refer to a computer program product includingat least one computer readable medium having computer program logic,such as computer-executable instructions, stored therein to cause acomputer system to perform one or more features and/or combinations offeatures disclosed herein. The computer readable medium may betransitory or non-transitory. An example of a transitory computerreadable medium may be a digital signal transmitted over a radiofrequency or over an electrical conductor, through a local or wide areanetwork, or through a network such as the Internet. An example of anon-transitory computer readable medium may be a compact disk, a flashmemory, or other data storage device.

Technologies disclosed herein determine privacy metrics that usepublicly available information and crowd-sourced data in conjunctionwith a user's own context and previous behavior to provide ratingsand/or recommendations to the user with regard to what action to take atthe establishments the user would like to patronize based on the user'sown comfort level with regard to privacy. The particular examples andscenarios used in this document are for ease of understanding and arenot to be limiting. The technologies described herein may be used inmany other contexts and situations that may or may not involve businessestablishments or even address privacy concerns directly. For example,the technologies described herein may be used to provide safety metricsrather than, or in addition to, privacy metrics (e.g., to warn thatthere is insufficient lighting in certain areas of an establishment orparking lot of an establishment, or to warn that a particular employeeor manager of an establishment is difficult to deal with or may cause aperson to feel uncomfortable). As another example, the technologiesdescribed herein may address privacy (or safety) concerns of a locationother than a retail establishment as used in examples herein, such as apublic park, a playground, a school or university, an amusement park, oran entertainment venue. In yet another example, a user using an emailclient or another application where the user may be about to shareinformation with an establishment (and/or possibly with a third partyestablishment handling the transmission of the information, such as anemail service) may wish to know how the establishment(s) handle privateinformation (social security numbers, email addresses, physicaladdresses, phone numbers, etc.). A service such as that described hereinmay provide a dynamic rating and/or recommendation for theseestablishment(s) prior to the user providing such personal information.Based on the rating, the user may be able to determine whether to sharecertain pieces of personal information using that particularapplication. Many other uses and/or types of establishments may also becontemplated.

An advantage of using the technologies described herein is that much ofthe data incorporated into the ratings and recommendations is data thatis not subjective, but instead based on actual behavior by consumers,which may be collected automatically by the consumer privacy ratingservice. In this way, privacy ratings and recommendations are not basedsolely on entered input, which may be biased and/or not necessarilyindicative of the whole truth, and the near real-time automatedcollection of consumer behavior makes the system dynamic in nature.Another advantage of the technologies described herein is that theratings and/or recommendations take a user's own privacy thresholds intoaccount. In other words, the ratings and/or recommendations arecustomized for each user. For the same establishment, the ratings and/orrecommendations for two different users may be determined to be quitedifferent depending on the known preferences of the two users. Manyother advantages may also be contemplated.

As used in this application and in the claims, a list of items joined bythe term “one or more of” can mean any combination of the listed terms.For example, the phrases “one or more of A, B or C” and “one or more ofA, B, and C” can mean A; B; C; A and B; A and C; B and C; or A, B and C.

The following examples pertain to further embodiments.

Example 1 may include a system for rating privacy of an establishment,comprising: one or more storage devices to store one or more ofprivacy-related information regarding one or more establishments oruser-related data; and a privacy score generator to: receive a locationdesignation of a user device from the user device; obtainprivacy-related information regarding one or more establishments thatare in proximity of the location designation from the one or morestorage devices; and generate one or more privacy score vectoralgorithms for the one or more establishments based on theprivacy-related information regarding the one or more establishments.

Example 2 may include the subject matter of Example 1, wherein theprivacy score generator is further to provide the one or more privacyscore vector algorithms to the user device.

Example 3 may include the subject matter of Example 1, wherein theprivacy score generator is further to: obtain the privacy profile of theuser from the one or more storage devices or the user device; determineone or more privacy scores for the one or more establishments byapplying the privacy profile of the user to the one or more privacyscore vector algorithms; and provide the one or more privacy scores tothe user device.

Example 4 may include the subject matter of Example 3, wherein theprivacy score generator is further to: receive a request from the userdevice for details regarding a particular privacy score of the one ormore privacy scores; and provide the details regarding the particularprivacy score to the user device.

Example 5 may include the subject matter of Example 3 or Example 4,wherein the privacy profile of the user includes one or more of:user-provided preferences regarding privacy, user-provided informationregarding the user's experience with various establishments or otherestablishments similar to the various establishments, or automaticallyobtained information regarding the user's behavior with regard to thevarious establishments or other establishments similar to the variousestablishments.

Example 6 may include the subject matter of Example 5, wherein theuser-provided information includes one or more of: the types ofestablishments that the user frequents, the types of transactions theuser conducts at the types of establishments, or information regardingthe user's privacy-related experiences at various establishments.

Example 7 may include the subject matter of Example 5 or Example 6,wherein the user-provided preferences include one or more of: the user'scomfort level with cameras or other surveillance devices used atestablishments; the user's comfort level with one or more of facialanalysis, gender recognition, gender profiling, age recognition, ageprofiling, or dress profiling; the user's comfort level with providingto establishments one or more of the user's name, the user's birthdate,the user's phone number, the user's email address, the user's zip code,the user's gender, the names of the user's children, or the ages of theuser's children, or the user's comfort level with allowingestablishments to keep track of the user's transactions.

Example 8 may include the subject matter of any one of Examples 3-7,wherein the privacy score generator is further to: generate aprivacy-related recommendation regarding a particular establishmentbased on the privacy-related information and the privacy profile of theuser; and provide the privacy-related recommendation to the user device.

Example 9 may include the subject matter of Example 8, wherein theprivacy-related recommendation includes one or more of: a recommendationto enter the establishment without a privacy concern, a recommendationto not enter the establishment, a recommendation to enter theestablishment but not use a loyalty card of the establishment, arecommendation to enter the establishment but use the loyalty card onlyfor specific purchases, a recommendation to enter the establishment butnot provide one or more items of personal information, a recommendationto enter the establishment but to avoid specific areas of theestablishment, or a recommendation to enter the establishment but toavoid certain employees of the establishment.

Example 10 may include the subject matter of Example 8 or Example 9,wherein the privacy score generator is further to: obtain outcomeinformation regarding the user's behavior regarding the establishmentafter receiving the privacy-related recommendation, wherein the outcomeinformation is based on one or more of information automatically derivedfrom the user's actions with regard to the establishment ormanually-provided information from the user regarding the user's actionswith regard to the establishment; and update the privacy-relatedinformation regarding the establishment based on the outcomeinformation.

Example 11 may include the subject matter of Example 10, wherein theprivacy score generator is further to update the privacy profile of theuser based on the outcome information.

Example 12 may include the subject matter of any one of Examples 3-11,wherein the privacy profile of the user includes one or more differingprivacy profiles each relevant to different circumstances.

Example 13 may include the subject matter of any one of Examples 3-12,wherein the privacy profile of the user is to mirror a privacy profileof another user who has an established relationship with the user.

Example 14 may include the subject matter of any one of Examples 3-13,wherein the privacy score comprises at least one of: a numerical rating;a grade; an indication of high, medium, or low; or an object rating.

Example 15 may include the subject matter of any one of Examples 1-14,wherein the privacy score generator is further to: collect at least aportion of the privacy-related information from electronicallyaccessible data sources.

Example 16 may include the subject matter of Example 15, wherein thedata sources include data sources of one or more of advocacy groups,watchdogs, privacy-related blogs, news services, or the one or moreestablishments.

Example 17 may include the subject matter of Example 15 or Example 16,wherein data collected from the data sources includes one or more ofelectronically accessible privacy statements of establishments, pledgesto consumers by establishments, data policies of the establishments,warnings regarding establishments, news stories regardingestablishments.

Example 18 may include the subject matter of any one of Examples 1-17,wherein the privacy-related information regarding the one or moreestablishments includes one or more of submissions of one or more users,wherein the one or more submissions include one or more of photographs,videos, positive feedback, negative feedback, or general comments of theone or more users regarding the one or more establishments.

Example 19 may include the subject matter of any one of Examples 1-18,wherein the privacy-related information regarding the one or moreestablishments includes automatically obtained data regarding behaviorof one or more users with regard to the one or more establishments.

Example 20 may include the subject matter of any one of Examples 1-19,wherein each establishment of the one or more establishments includesone of: a retail store, a restaurant, a website of a retail store, amobile application of a retail store, a website of a restaurant, or amobile application of a restaurant.

Example 21 may include the subject matter of any one of Examples 1-20,wherein being in the proximity of a particular establishment includes atleast one of: within a given distance of known coordinates of theestablishment, inside the establishment, at a website of theestablishment, or within a user-entered zip code that includes theestablishment.

Example 22 may include the subject matter of any one of Examples 1-21,wherein the location designation includes one or more of: globalpositioning system (GPS) coordinates, one or more zip codes, one or moreestablishment names, or one or more establishment types.

Example 23 may include at least one computer-readable storage mediumencoded with a computer program including instructions that whenexecuted on a processor, cause the processor to: receive a locationdesignation of a user device from the user device; obtainprivacy-related information regarding one or more establishments inproximity of the location designation from one or more electronicallyaccessible storage devices; and generate one or more privacy scorevector algorithms for the one or more establishments based on theprivacy-related information regarding the one or more establishments.

Example 24 may include the subject matter of Example 23, wherein thecomputer program includes instructions to further cause the processor toprovide the one or more privacy score vector algorithms to the userdevice.

Example 25 may include the subject matter of Example 23, wherein thecomputer program includes instructions to further cause the processorto: obtain the privacy profile of the user from the one or moreelectronically accessible storage devices or the user device; determineone or more privacy scores for the one or more establishments byapplying the privacy profile of the user to the one or more privacyscore vector algorithms; and provide the one or more privacy scores tothe user device.

Example 26 may include the subject matter of Example 25, wherein thecomputer program includes instructions to further cause the processorto: generate a privacy-related recommendation regarding a particularestablishment based on the privacy-related information and the privacyprofile of the user; and provide the privacy-related recommendation tothe user device.

Example 27 may include an apparatus for rating privacy of anestablishment, comprising: means for receiving a location designation ofa user device from the user device; means for obtaining privacy-relatedinformation regarding one or more establishments in proximity of thelocation designation from one or more electronically accessible storagedevices; and means for generating one or more privacy score vectoralgorithms for the one or more establishments based on theprivacy-related information regarding the one or more establishments.

In Example 28, Example 27 may optionally include means for providing theone or more privacy score vector algorithms to the user device.

In Example 29, Example 27 may optionally include means for obtaining theprivacy profile of the user from the one or more electronicallyaccessible storage devices or the user device; means for determining oneor more privacy scores for the one or more establishments by applyingthe privacy profile of the user to the one or more privacy score vectoralgorithms; and means for providing the one or more privacy scores tothe user device.

In Example 30, Example 29 may optionally include means for generating aprivacy-related recommendation regarding a particular establishmentbased on the privacy-related information and the privacy profile of theuser; and means for providing the privacy-related recommendation to theuser device.

Example 31 may include a machine-implemented method of rating privacy ofone or more establishments, comprising: receiving a location designationof a user device from the user device; obtaining privacy-relatedinformation regarding one or more establishments in proximity of thelocation designation from one or more electronically accessible storagedevices; and generating one or more privacy score vector algorithms forthe one or more establishments based on the privacy-related informationregarding the one or more establishments.

In Example 32, Example 31 may optionally include providing the one ormore privacy score vector algorithms to the user device.

In Example 33, Example 31 may optionally include: obtaining the privacyprofile of the user from the one or more electronically accessiblestorage devices or the user device; determining one or more privacyscores for the one or more establishments by applying the privacyprofile of the user to the one or more privacy score vector algorithms;and providing the one or more privacy scores to the user device.

In Example 34, Example 33 may optionally include generating aprivacy-related recommendation regarding a particular establishmentbased on the privacy-related information and the privacy profile of theuser; and providing the privacy-related recommendation to the userdevice.

Example 35 may include at least one machine readable storage mediumcomprising a plurality of instructions that in response to beingexecuted on a computing device, cause the computing device to carry outa method according to any one of Examples 31-34.

Example 36 may include an apparatus configured to perform the method ofany one of the Examples 31-34.

Example 37 may include a computer system to perform the method of anyone of Examples 31-34.

Example 38 may include a machine to perform the method of any one ofExamples 31-34.

Example 39 may include an apparatus comprising means for performing themethod of any one of Examples 31-34.

Example 40 may include a computing device comprising memory and achipset configured to perform the method of any one of Examples 31-34.

Example 41 may include a user device for rating privacy of anestablishment, comprising: a processor and memory; a user interface; anda communication system configured to interface with a communicationnetwork and one or more of the processor or the user interface, whereinthe processor is to: provide a location designation to a consumerprivacy rating server; and obtain one or more privacy scores for one ormore respective establishments, wherein a location identified by thelocation designation is in proximity of the one or more establishments,and wherein the privacy scores are based on privacy-related informationregarding the one or more establishments and a privacy profile of a userof the user device.

Example 42 may include the subject matter of Example 41, wherein theobtaining the one or more privacy scores comprises receiving the one ormore privacy scores from the consumer privacy rating server; and whereinthe processor is further to display the one or more privacy scores.

Example 43 may include the subject matter of Example 42, wherein theprocessor is further to: receive, from the consumer privacy ratingserver, a privacy-related recommendation regarding a particularestablishment based on the privacy-related information and the privacyprofile of the user; and display the privacy-related recommendation.

Example 44 may include the subject matter of Example 41, wherein theobtaining the one or more privacy scores comprises: receiving, from theconsumer privacy rating server, one or more vector algorithms fordetermining the one or more privacy scores, wherein the privacy-relatedinformation regarding the one or more establishments is incorporatedinto the one or more vector algorithms; and determining the one or moreprivacy scores by applying the privacy profile of the user to the one ormore vector algorithms; and wherein the processor is further to displaythe one or more privacy scores.

Example 45 may include the subject matter of Example 44, wherein theprocessor is further to: generate a privacy-related recommendationregarding a particular establishment based on the privacy-relatedinformation and the privacy profile of the user; and display theprivacy-related recommendation.

Example 46 may include at least one computer-readable storage mediumencoded with a computer program including instructions that whenexecuted on a processor, cause the processor to: provide a locationdesignation of a user device to a consumer privacy rating server; andobtain one or more privacy scores for one or more respectiveestablishments, wherein a location identified by the locationdesignation is in proximity of the one or more establishments, andwherein the privacy scores are based on privacy-related informationregarding the one or more establishments and a privacy profile of a userof the user device.

Example 47 may include the subject matter of Example 46, wherein theobtaining the one or more privacy scores comprises receiving the one ormore privacy scores from the consumer privacy rating server; and whereinthe computer program includes instructions to further cause theprocessor to display the one or more privacy scores.

Example 48 may include the subject matter of Example 47, wherein thecomputer program includes instructions to further cause the processorto: receive, from the consumer privacy rating server, a privacy-relatedrecommendation regarding a particular establishment based on theprivacy-related information and the privacy profile of the user; anddisplay the privacy-related recommendation.

Example 49 may include the subject matter of Example 46, wherein theobtaining the one or more privacy scores comprises: receiving, from theconsumer privacy rating server, one or more vector algorithms fordetermining the one or more privacy scores, wherein the privacy-relatedinformation regarding the one or more establishments is incorporatedinto the one or more vector algorithms; and determining the one or moreprivacy scores by applying the privacy profile of the user to the one ormore vector algorithms; and wherein the computer program includesinstructions to further cause the processor to display the one or moreprivacy scores.

Example 50 may include the subject matter of Example 49, wherein thecomputer program includes instructions to further cause the processorto: generate a privacy-related recommendation regarding a particularestablishment based on the privacy-related information and the privacyprofile of the user; and display the privacy-related recommendation.

Example 51 may include an apparatus for rating privacy of anestablishment, comprising: means for providing a location designation ofa user device to a consumer privacy rating server; and means forobtaining one or more privacy scores for one or more respectiveestablishments, wherein a location identified by the locationdesignation is in proximity of the one or more establishments, andwherein the privacy scores are based on privacy-related informationregarding the one or more establishments and a privacy profile of a userof the user device.

Example 52 may include the subject matter of Example 51, wherein themeans for obtaining the one or more privacy scores comprises means forreceiving the one or more privacy scores from the consumer privacyrating server; and wherein the apparatus further comprises means fordisplaying the one or more privacy scores.

In Example 53, Example 52 may optionally include means for receiving,from the consumer privacy rating server, a privacy-relatedrecommendation regarding a particular establishment based on theprivacy-related information and the privacy profile of the user; andmeans for displaying the privacy-related recommendation.

Example 54 may include the subject matter of Example 51, wherein themeans for obtaining the one or more privacy scores comprises: means forreceiving, from the consumer privacy rating server, one or more vectoralgorithms for determining the one or more privacy scores, wherein theprivacy-related information regarding the one or more establishments isincorporated into the one or more vector algorithms; and means fordetermining the one or more privacy scores by applying the privacyprofile of the user to the one or more vector algorithms; and whereinthe apparatus further comprises means for displaying the one or moreprivacy scores.

In Example 55, Example 54 may optionally include means for generating aprivacy-related recommendation regarding a particular establishmentbased on the privacy-related information and the privacy profile of theuser; and means for displaying the privacy-related recommendation.

Example 56 may include a method for rating privacy of an establishment,comprising: providing a location designation of a user device to aconsumer privacy rating server; and obtaining one or more privacy scoresfor one or more respective establishments, wherein a location identifiedby the location designation is in proximity of the one or moreestablishments, and wherein the privacy scores are based onprivacy-related information regarding the one or more establishments anda privacy profile of a user of the user device.

Example 57 may include the subject matter of Example 56, wherein theobtaining the one or more privacy scores comprises receiving the one ormore privacy scores from the consumer privacy rating server; and whereinthe method further comprises displaying the one or more privacy scores.

In Example 58, Example 57 may optionally include receiving, from theconsumer privacy rating server, a privacy-related recommendationregarding a particular establishment based on the privacy-relatedinformation and the privacy profile of the user; and displaying theprivacy-related recommendation.

Example 59 may include the subject matter of Example 56, wherein theobtaining the one or more privacy scores comprises: receiving, from theconsumer privacy rating server, one or more vector algorithms fordetermining the one or more privacy scores, wherein the privacy-relatedinformation regarding the one or more establishments is incorporatedinto the one or more vector algorithms; and determining the one or moreprivacy scores by applying the privacy profile of the user to the one ormore vector algorithms; and wherein the method further comprisesdisplaying the one or more privacy scores.

In Example 60, Example 59 may optionally include generating aprivacy-related recommendation regarding a particular establishmentbased on the privacy-related information and the privacy profile of theuser; and displaying the privacy-related recommendation.

Example 61 may include at least one machine readable storage mediumcomprising a plurality of instructions that in response to beingexecuted on a computing device, cause the computing device to carry outa method according to any one of Examples 56-60.

Example 62 may include an apparatus configured to perform the method ofany one of the Examples 56-60.

Example 63 may include a computer system to perform the method of anyone of Examples 56-60.

Example 64 may include a machine to perform the method of any one ofExamples 56-60.

Example 65 may include an apparatus comprising means for performing themethod of any one of Examples 56-60.

Example 66 may include a computing device comprising memory and achipset configured to perform the method of any one of Examples 56-60.

1-30. (canceled)
 31. A system for rating privacy of an establishment,comprising: one or more storage devices to store one or more ofprivacy-related information regarding one or more establishments oruser-related data; and a privacy score generator to: receive a locationdesignation of a user device from the user device; obtainprivacy-related information regarding one or more establishments thatare in proximity of the location designation from the one or morestorage devices; and generate one or more privacy score vectoralgorithms for the one or more establishments based on theprivacy-related information regarding the one or more establishments.32. The system of claim 31, wherein the privacy score generator isfurther to: provide the one or more privacy score vector algorithms tothe user device.
 33. The system of claim 31, wherein the privacy scoregenerator is further to: obtain the privacy profile of the user from theone or more storage devices or the user device; determine one or moreprivacy scores for the one or more establishments by applying theprivacy profile of the user to the one or more privacy score vectoralgorithms; and provide the one or more privacy scores to the userdevice.
 34. The system of claim 33, wherein the privacy score generatoris further to: receive a request from the user device for detailsregarding a particular privacy score of the one or more privacy scores;and provide the details regarding the particular privacy score to theuser device.
 35. The system of claim 33, wherein the privacy profile ofthe user includes one or more of: user-provided preferences regardingprivacy, user-provided information regarding the user's experience withvarious establishments or other establishments similar to the variousestablishments, or automatically obtained information regarding theuser's behavior with regard to the various establishments or otherestablishments similar to the various establishments.
 36. The system ofclaim 35, wherein the user-provided information includes one or more of:the types of establishments that the user frequents, the types oftransactions the user conducts at the types of establishments, orinformation regarding the user's privacy-related experiences at variousestablishments.
 37. The system of claim 36, wherein the user-providedpreferences include one or more of: the user's comfort level withcameras or other surveillance devices used at establishments; the user'scomfort level with one or more of facial analysis, gender recognition,gender profiling, age recognition, age profiling, or dress profiling;the user's comfort level with providing to establishments one or more ofthe user's name, the user's birthdate, the user's phone number, theuser's email address, the user's zip code, the user's gender, the namesof the user's children, or the ages of the user's children; or theuser's comfort level with allowing establishments to keep track of theuser's transactions.
 38. The system of claim 33, wherein the privacyscore generator is further to: generate a privacy-related recommendationregarding a particular establishment based on the privacy-relatedinformation and the privacy profile of the user; and provide theprivacy-related recommendation to the user device.
 39. The system ofclaim 38, wherein the privacy score generator is further to: obtainoutcome information regarding the user's behavior regarding theestablishment after receiving the privacy-related recommendation,wherein the outcome information is based on one or more of informationautomatically derived from the user's actions with regard to theestablishment or manually-provided information from the user regardingthe user's actions with regard to the establishment; and update theprivacy-related information regarding the establishment based on theoutcome information.
 40. The system of claim 39, wherein the privacyscore generator is further to: update the privacy profile of the userbased on the outcome information.
 41. The system of claim 33, whereinthe privacy profile of the user includes one or more differing privacyprofiles each relevant to different circumstances.
 42. The system ofclaim 33, wherein the privacy profile of the user is to mirror a privacyprofile of another user who has an established relationship with theuser.
 43. The system of claim 33, wherein the privacy score comprises atleast one of: a numerical rating; a grade; an indication of high,medium, or low; or an object rating.
 44. The system of claim 31, whereinthe privacy score generator is further to: collect at least a portion ofthe privacy-related information from electronically accessible datasources.
 45. The system of claim 44, wherein the data sources includedata sources of one or more of advocacy groups, watchdogs,privacy-related blogs, news services, or the one or more establishments.46. The system of claim 45, wherein data collected from the data sourcesincludes one or more of electronically accessible privacy statements ofestablishments, pledges to consumers by establishments, data policies ofthe establishments, warnings regarding establishments, news storiesregarding establishments.
 47. The system of claim 31, wherein theprivacy-related information regarding the one or more establishmentsincludes one or more of submissions of one or more users, wherein theone or more submissions include one or more of photographs, videos,positive feedback, negative feedback, or general comments of the one ormore users regarding the one or more establishments.
 48. The system ofclaim 31, wherein the privacy-related information regarding the one ormore establishments includes automatically obtained data regardingbehavior of one or more users with regard to the one or moreestablishments.
 49. The system of claim 31, wherein being in theproximity of a particular establishment includes at least one of: withina given distance of known coordinates of the establishment, inside theestablishment, at a website of the establishment, or within auser-entered zip code that includes the establishment.
 50. Amachine-implemented method of rating privacy of one or moreestablishments, comprising: receiving a location designation of a userdevice from the user device; obtaining privacy-related informationregarding one or more establishments in proximity of the locationdesignation from one or more electronically accessible storage devices;and generating one or more privacy score vector algorithms for the oneor more establishments based on the privacy-related informationregarding the one or more establishments.
 51. The method of claim 50,further comprising: providing the one or more privacy score vectoralgorithms to the user device.
 52. The method of claim 50, furthercomprising: obtaining the privacy profile of the user from the one ormore electronically accessible storage devices or the user device;determining one or more privacy scores for the one or moreestablishments by applying the privacy profile of the user to the one ormore privacy score vector algorithms; and providing the one or moreprivacy scores to the user device.
 53. The method of claim 52, furthercomprising: generating a privacy-related recommendation regarding aparticular establishment based on the privacy-related information andthe privacy profile of the user; and providing the privacy-relatedrecommendation to the user device.
 54. At least one non-transitorycomputer-readable storage medium encoded with a computer programincluding instructions that when executed on a processor, cause theprocessor to: receive a location designation of a user device from theuser device; obtain privacy-related information regarding one or moreestablishments in proximity of the location designation from one or moreelectronically accessible storage devices; and generate one or moreprivacy score vector algorithms for the one or more establishments basedon the privacy-related information regarding the one or moreestablishments.
 55. An apparatus for rating privacy of an establishment,comprising: means for receiving a location designation of a user devicefrom the user device; means for obtaining privacy-related informationregarding one or more establishments in proximity of the locationdesignation from one or more electronically accessible storage devices;and means for generating one or more privacy score vector algorithms forthe one or more establishments based on the privacy-related informationregarding the one or more establishments.